arXiv subjects

Raimund Rolfes

Publications and source records attributed to Raimund Rolfes.

8 recordsLinked to original sources

arXiv2026

Bridging nanoparticle morphology and viscoelastic behavior in epoxy nanocomposites: A coarse-grained simulation-informed constitutive model

Accurate prediction of the material behavior of polymer nanocomposites under various thermomechanical loading conditions is increasingly demanded for engineering applications. This study proposes an integrated framework combining coarse-grained (CG) molecular simulations and experimental testing to develop predictive constitutive models for nanoparticle/epoxy nanocomposites. The key contribution of this work lies in characterizing the influence of nanoparticle content and agglomerate size on the rate- and temperature-dependent behavior of nanocomposites, enabled by large-scale CG simulations. The proposed framework successfully captures the material response, including nonlinear hyperelasticity, softening behavior, and rate- and temperature-dependent properties, across a broad range of strain rates, temperatures, and nanoparticle sizes and weight fractions. The predictive capability of the CG simulation-informed constitutive model is validated using additional experimental data that were not included in the parameter identification process. By reducing reliance on extensive experimental testing while maintaining high accuracy, this simulation-driven approach offers an efficient pathway for developing robust, predictive constitutive models for designing and optimizing advanced nanocomposites.

cs.CE↗

arXiv2026

A hybrid electromechanical phase-field and deep learning framework for predicting fracture in dielectric nanocomposites

The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This work presents a hybrid modeling framework that combines an electromechanical phase-field fracture model with deep learning-based surrogate modeling to predict fracture evolution in dielectric nanocomposite plates. The underlying finite element simulations capture the coupling between mechanical deformation and electrical field perturbations caused by cracks, using a variational phase-field formulation. High-fidelity simulation outputs - namely, phase-field damage variables and electric potential fields -- are used to train convolutional neural networks (CNNs) with ResNet-U-Net architectures for pixel-wise segmentation of crack paths. The study systematically compares the performance of CNNs trained on phase-field versus electric potential data across multiple ResNet backbones. The results reveal that electric potential fields, although they encode damage indirectly, offer superior segmentation accuracy, faster convergence, and enhanced generalization, owing to their smoother gradient distribution and global spatial coverage. The proposed framework significantly reduces computational costs while preserving high accuracy, offers potential when appropriately adapted for sensor-based input data.

physics.comp-ph↗

arXiv2024

A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites

This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. To accomplish this, a long short-term memory network is combined with a feed-forward neural network to predict internal variables required for characterizing the internal dissipation of the nanocomposite materials. In addition, another feed-forward neural network is used to indicate the free-energy function, which enables defining the thermodynamic state of the entire system. The PIDL model is initially developed for the three-dimensional case by generating synthetic data from a classical constitutive model. The model is then trained by extracting the data directly from cyclic loading-unloading experimental tests. Numerical examples show that the PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions.

cs.LG↗

arXiv2024

Cyclic viscoelastic-viscoplastic behavior of epoxy nanocomposites under hygrothermal conditions: A phase-field fracture model

In this study, a finite deformation phase-field formulation is developed to investigate the effect of hygrothermal conditions on the viscoelastic-viscoplastic fracture behavior of epoxy nanocomposites under cyclic loading. The formulation incorporates a definition of the Helmholtz free energy, which considers the effect of nanoparticles, moisture content, and temperature. The free energy is additively decomposed into a deviatoric equilibrium, a deviatoric non-equilibrium, and a volumetric contribution, with distinct definitions for tension and compression. The proposed derivation offers a realistic modeling of damage and viscoplasticity mechanisms in the nanocomposites by coupling the phase-field damage model with a modified crack driving force and a viscoelastic-viscoplastic model. Numerical simulations are conducted to study the cyclic force-displacement response of both dry and saturated boehmite nanoparticle (BNP)/epoxy samples, considering BNP contents and temperature. Comparing numerical results with experimental data shows good agreement at various BNP contents. In addition, the predictive capability of the phase-field model is evaluated through simulations of single-edge notched nanocomposite plates subjected to monolithic tensile and shear loading.

cs.CE↗

arXiv2023

A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content

In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress-strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force-displacement response of nanoparticle/ epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents.

cs.LG↗

arXiv2019

A Framework for Data-Driven Computational Dynamics Based on Nonlinear Optimization

In this article, we present an extension of the formulation recently developed by the authors (A Framework for Data-Driven Computational Mechanics Based on Nonlinear Optimization, arXiv:1910.12736 [math.NA]) to the structural dynamics setting. Inspired by a structure-preserving family of variational integrators, our new formulation relies on a discrete balance equation that establishes the dynamic equilibrium. From this point of departure, we first derive an "exact" discrete-continuous nonlinear optimization problem that works directly with data sets. We then develop this formulation further into an "approximate" nonlinear optimization problem that relies on a general constitutive model. This underlying model can be identified from a data set in an offline phase. To showcase the advantages of our framework, we specialize our methodology to the case of a geometrically exact beam formulation that makes use of all elements of our approach. We investigate three numerical examples of increasing difficulty that demonstrate the excellent computational behavior of the proposed framework and motivate future research in this direction.

math.NA↗

arXiv2019

A Framework for Data-Driven Computational Mechanics Based on Nonlinear Optimization

Data-Driven Computational Mechanics is a novel computing paradigm that enables the transition from standard data-starved approaches to modern data-rich approaches. At this early stage of development, one can distinguish two mainstream directions. The first one relies on a discrete-continuous optimization problem and seeks to assign to each material point a point in the phase space that satisfies compatibility and equilibrium, while being closest to the data set provided. The second one is a data driven inverse approach that seeks to reconstruct a constitutive manifold from data sets by manifold learning techniques, relying on a well-defined functional structure of the underlying constitutive law. In this work, we propose a third route that combines the strengths of the two existing directions and mitigates some of their weaknesses. This is achieved by the formulation of an approximate nonlinear optimization problem, which can be robustly solved, is computationally efficient, and does not rely on any special functional structure of the reconstructed constitutive manifold. Additional benefits include the natural incorporation of kinematic constraints and the possibility to operate with implicitly defined stress-strain relations. We discuss important mathematical aspects of our approach for a data-driven truss element and investigate its key numerical behavior for a data-driven beam element that makes use of all components of our methodology.

math.NA↗

arXiv2019

A new conservative/dissipative time integration scheme for nonlinear mechanical systems

We present a conservative/dissipative time integration scheme for nonlinear mechanical systems. Starting from a weak form, we derive algorithmic forces and velocities that guarantee the desired conservation/dissipation properties. Our approach relies on a collection of linearly constrained quadratic programs defining high order correction terms that modify, in the minimum possible way, the classical midpoint rule so as to guarantee the strict energy conservation/dissipation properties. The solution of these programs provides explicit formulas for the algorithmic forces and velocities which can be easily incorporated into existing implementations. Similarities and differences between our approach and well-established methods are discussed as well. The approach, suitable for reduced-order models, finite element models, or multibody systems, is tested and its capabilities are illustrated by means of several examples.

math.NA↗